ISBN: 9780521847032 - applied asymptotics: case studies in small-sample statistics (cambridge series in statistical and probabilistic mathematics) (5 Ergebnisse)

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:9780521847032.…

Zustand: Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Zustand: New. New likelihood-based methods now allow very accurate approximations in finite samples and this book illustrates with realistic examples and case studies how to use the new theory. The treatment is oriented towards practice and is accompanied by code in the .

Hardcover. Zustand: Brand New. illustrated edition. 248 pages. 10.00x6.50x0.50 inches. In Stock.

Buch. Zustand: Neu. Neuware - In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.…